We create a dataset for this multi-hop relation extraction from a multi-hop question-answering (QA) dataset WikiHop [1]}. [1]} defined the multi-hop QA task as follows: Given a set of supporting documents \(D_s\)  and a set of candidate answer \(C_a\)  which are mentioned in \(D_s\) , the goal is to find the correct answer \(a^* \in C_a\)  by drawing on the supporting documents. They used Wikipedia articles and Wikidata [3]} tuples for creating this dataset. Each positive tuple \((e_s, e_o, r_p)\)  in Wikidata has two entities, a subject entity \(e_s\)  and an object entity \(e_o\) , and a positive relation \(r_p\)  between the subject and object entity. The questions are created by combining the subject entity \(e_s\)  and the relation \(r_p\) , and the object entity \(e_o\)  is the correct answer \(a^*\)  for a given question. The other candidate answers are carefully chosen from Wikidata entities so that they have a similar type as the correct answer. The supporting documents are chosen in such a way that at least two documents are needed to find the correct answer. This means the subject entity \(e_s\)  and the object entity \(e_o\)  do not appear in the same document. They used a bipartite graph partition technique to create the dataset. In this bipartite graph, vertices on one side correspond to Wikidata entities, and vertices on the other side correspond to Wikipedia articles. An edge is created between an entity vertex and a document vertex if this document contains the entity. As we traverse the graph starting from vertex \(e_s\) , it visits many document vertices and entity vertices. This constitutes the supporting document set and candidate answer set. If the candidate answer set does not contain the object entity \(e_o\)  which is the correct answer, this instance is discarded. They also limited the length of the traversal to 3 documents. [1]} only released the supporting documents, questions, and candidate answers for their dataset. They did not release the connecting entities.
